{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "seven-destination",
   "metadata": {},
   "source": [
    "## 加载CSV文件与快速查看数据结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "worth-fisher",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用pandas加载数据\n",
    "#%matplotlib inline#取消注释则使用jupyter内部的图渲染\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def load_housing_data(path):\n",
    "    return pd.read_csv(path)\n",
    "housing=load_housing_data(\"./csv/housing.csv\")\n",
    "housing.head()#显示数据集表头,并显示前5条数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organized-secret",
   "metadata": {},
   "source": [
    "## 获取数据集简单描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "divided-deviation",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #快速获取数据集的简单描述，总行数、每个属性的类型和空值的数量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "american-custom",
   "metadata": {},
   "source": [
    "## 分析列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "moral-metropolitan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts() #分析列，将值分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "passing-minute",
   "metadata": {},
   "source": [
    "## 简单数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "instant-minneapolis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()#简单数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspended-heading",
   "metadata": {},
   "source": [
    "## 画直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "about-strength",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#直方图显示个属性\n",
    "housing.hist(bins=50,figsize=(20,15))#bins为柱子个数，figsize为图的大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grave-breeding",
   "metadata": {},
   "source": [
    "## 创建测试集\n",
    "> 在使用数据集时，我们通常会将其分为训练集与测试集，使用训练集来训练模型，使用测试集来验证与评价我们的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "creative-radiation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set:\n",
      "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
      "17875    -121.99     37.40                35.0       1845.0           325.0   \n",
      "9360     -122.53     37.95                22.0       7446.0          1979.0   \n",
      "4338     -118.31     34.08                26.0       1609.0           534.0   \n",
      "986      -121.85     37.72                43.0        228.0            40.0   \n",
      "8129     -118.17     33.80                26.0       1589.0           380.0   \n",
      "\n",
      "       population  households  median_income  median_house_value  \\\n",
      "17875      1343.0       317.0         5.3912            235300.0   \n",
      "9360       2980.0      1888.0         3.5838            271300.0   \n",
      "4338       1868.0       497.0         2.7038            227100.0   \n",
      "986          83.0        42.0        10.3203            400000.0   \n",
      "8129        883.0       366.0         3.5313            187500.0   \n",
      "\n",
      "      ocean_proximity  \n",
      "17875       <1H OCEAN  \n",
      "9360         NEAR BAY  \n",
      "4338        <1H OCEAN  \n",
      "986            INLAND  \n",
      "8129       NEAR OCEAN  \n",
      "test_set:\n",
      "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
      "15722    -122.46     37.78                47.0       1682.0           379.0   \n",
      "19685    -121.61     39.14                44.0       2035.0           476.0   \n",
      "6989     -118.04     33.97                29.0       2376.0           700.0   \n",
      "5804     -118.25     34.15                13.0       1107.0           479.0   \n",
      "5806     -118.26     34.14                29.0       3431.0          1222.0   \n",
      "\n",
      "       population  households  median_income  median_house_value  \\\n",
      "15722       837.0       375.0         5.2806            400000.0   \n",
      "19685      1030.0       453.0         1.4661             65200.0   \n",
      "6989       1968.0       680.0         2.6082            162500.0   \n",
      "5804        616.0       443.0         0.8185            187500.0   \n",
      "5806       4094.0      1205.0         2.2614            248100.0   \n",
      "\n",
      "      ocean_proximity  \n",
      "15722        NEAR BAY  \n",
      "19685          INLAND  \n",
      "6989        <1H OCEAN  \n",
      "5804        <1H OCEAN  \n",
      "5806        <1H OCEAN  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\"\"\"\n",
    "@param data 数据集\n",
    "@param test_ratio 测试集的比例\n",
    "\"\"\"\n",
    "\n",
    "def split_train_test(data,test_ratio,seed_num):\n",
    "    #使用numpy.random.permutation 来生成随机索引[ 0,len(data) ) 序列\n",
    "    np.random.seed(seed_num) \n",
    "    shuffled_indices=np.random.permutation(len(data))\n",
    "    #print(shuffled_indices) #例: [11146  6045 18951 ...   898 11036 14949]\n",
    "    #计算测试集样本个数\n",
    "    test_set_size=int(len(data)*test_ratio)\n",
    "    #分别得出训练集与测试集的索引\n",
    "    test_indices=shuffled_indices[:test_set_size]#测试集的索引，取前test_set_size个\n",
    "    train_indices=shuffled_indices[test_set_size:]#训练集的索引，取[test_set_size,...]个\n",
    "    #返回相对应索引实例组成的列表，再将二者以元组形式返回\n",
    "    return (data.iloc[train_indices],data.iloc[test_indices]) #元组的两个元素都是list\n",
    "\n",
    "(train_set,test_set)=split_train_test(housing,0.2,int(random.random()*10))\n",
    "print(\"train_set:\")\n",
    "print(train_set.head())\n",
    "print(\"test_set:\")\n",
    "print(test_set.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spoken-prior",
   "metadata": {},
   "source": [
    "> `因为数据集的划分依靠随机数，而随机数的生成依靠的种种子，如果我们想要其划分不变，就在生成随机数前种种子(固定数值)，不然每次划分情况都不一样`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "antique-arena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0\n",
       "1            1\n",
       "2            2\n",
       "3            3\n",
       "4            4\n",
       "         ...  \n",
       "20635    20635\n",
       "20636    20636\n",
       "20637    20637\n",
       "20638    20638\n",
       "20639    20639\n",
       "Name: index, Length: 20640, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.reset_index()[\"index\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greatest-indicator",
   "metadata": {},
   "source": [
    "## 利用Hash划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "interested-despite",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0         True\n",
      "1         True\n",
      "2        False\n",
      "3         True\n",
      "4         True\n",
      "         ...  \n",
      "20635     True\n",
      "20636     True\n",
      "20637     True\n",
      "20638     True\n",
      "20639     True\n",
      "Name: index, Length: 20640, dtype: bool, 0        False\n",
      "1        False\n",
      "2         True\n",
      "3        False\n",
      "4        False\n",
      "         ...  \n",
      "20635    False\n",
      "20636    False\n",
      "20637    False\n",
      "20638    False\n",
      "20639    False\n",
      "Name: index, Length: 20640, dtype: bool]\n",
      "[index                 16512\n",
      "longitude             16512\n",
      "latitude              16512\n",
      "housing_median_age    16512\n",
      "total_rooms           16512\n",
      "total_bedrooms        16348\n",
      "population            16512\n",
      "households            16512\n",
      "median_income         16512\n",
      "median_house_value    16512\n",
      "ocean_proximity       16512\n",
      "dtype: int64, index                 4128\n",
      "longitude             4128\n",
      "latitude              4128\n",
      "housing_median_age    4128\n",
      "total_rooms           4128\n",
      "total_bedrooms        4085\n",
      "population            4128\n",
      "households            4128\n",
      "median_income         4128\n",
      "median_house_value    4128\n",
      "ocean_proximity       4128\n",
      "dtype: int64]\n",
      "[0         True\n",
      "1         True\n",
      "2        False\n",
      "3         True\n",
      "4         True\n",
      "         ...  \n",
      "20635     True\n",
      "20636     True\n",
      "20637     True\n",
      "20638     True\n",
      "20639     True\n",
      "Name: index, Length: 20640, dtype: bool, 0        False\n",
      "1        False\n",
      "2         True\n",
      "3        False\n",
      "4        False\n",
      "         ...  \n",
      "20635    False\n",
      "20636    False\n",
      "20637    False\n",
      "20638    False\n",
      "20639    False\n",
      "Name: index, Length: 20640, dtype: bool]\n"
     ]
    }
   ],
   "source": [
    "import zlib\n",
    "def test_set_check(identifier,test_ratio):\n",
    "    return zlib.crc32(np.int64(identifier))&0xffffffff<test_ratio*2**32\n",
    "# id被hash到[0,test_ratio*2**32] 从而数据集整体是被打乱的\n",
    "#print(test_set_check(36548,0.2));# False 不属于测试集\n",
    "\n",
    "def split_train_test_by_id(data,test_ratio,id_column):\n",
    "    ids=data[id_column] #取出列名为id_column的一列\n",
    "    # hash映射\n",
    "    in_test_set=ids.apply(\n",
    "        lambda id_:test_set_check(id_,test_ratio)#lambda表达式\n",
    "    );\n",
    "    print([~in_test_set,in_test_set])\n",
    "    #在in_test_set中in_test_set[index]==True则代表data[index]为测试集\n",
    "    return data.loc[~in_test_set],data.loc[in_test_set]\n",
    "\n",
    "housing_with_id=housing.reset_index()\n",
    "(train_set,test_set)=split_train_test_by_id(housing_with_id,0.2,\"index\")\n",
    "print([train_set.count(),test_set.count()]);# 13225 ，3287 比例分开，且是随机分开\n",
    "\n",
    "#But you don't have to use the index to hash, you can customize it\n",
    "housing_with_id[\"id\"]=housing[\"longitude\"]*1000+housing[\"latitude\"]\n",
    "train_set,test_set=split_train_test_by_id(housing_with_id,0.2,\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transsexual-heading",
   "metadata": {},
   "source": [
    "## 分层抽样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "suffering-image",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
      "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
      "18632    -121.93     37.05                14.0        679.0           108.0   \n",
      "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
      "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
      "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
      "...          ...       ...                 ...          ...             ...   \n",
      "6563     -118.13     34.20                46.0       1271.0           236.0   \n",
      "12053    -117.56     33.88                40.0       1196.0           294.0   \n",
      "13908    -116.40     34.09                 9.0       4855.0           872.0   \n",
      "11159    -118.01     33.82                31.0       1960.0           380.0   \n",
      "15775    -122.45     37.77                52.0       3095.0           682.0   \n",
      "\n",
      "       population  households  median_income  median_house_value  \\\n",
      "17606       710.0       339.0         2.7042            286600.0   \n",
      "18632       306.0       113.0         6.4214            340600.0   \n",
      "14650       936.0       462.0         2.8621            196900.0   \n",
      "3230       1460.0       353.0         1.8839             46300.0   \n",
      "3555       4459.0      1463.0         3.0347            254500.0   \n",
      "...           ...         ...            ...                 ...   \n",
      "6563        573.0       210.0         4.9312            240200.0   \n",
      "12053      1052.0       258.0         2.0682            113000.0   \n",
      "13908      2098.0       765.0         3.2723             97800.0   \n",
      "11159      1356.0       356.0         4.0625            225900.0   \n",
      "15775      1269.0       639.0         3.5750            500001.0   \n",
      "\n",
      "      ocean_proximity  \n",
      "17606       <1H OCEAN  \n",
      "18632       <1H OCEAN  \n",
      "14650      NEAR OCEAN  \n",
      "3230           INLAND  \n",
      "3555        <1H OCEAN  \n",
      "...               ...  \n",
      "6563           INLAND  \n",
      "12053          INLAND  \n",
      "13908          INLAND  \n",
      "11159       <1H OCEAN  \n",
      "15775        NEAR BAY  \n",
      "\n",
      "[16512 rows x 10 columns]        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
      "5241     -118.39     34.12                29.0       6447.0          1012.0   \n",
      "10970    -117.86     33.77                39.0       4159.0           655.0   \n",
      "20351    -119.05     34.21                27.0       4357.0           926.0   \n",
      "6568     -118.15     34.20                52.0       1786.0           306.0   \n",
      "13285    -117.68     34.07                32.0       1775.0           314.0   \n",
      "...          ...       ...                 ...          ...             ...   \n",
      "20519    -121.53     38.58                33.0       4988.0          1169.0   \n",
      "17430    -120.44     34.65                30.0       2265.0           512.0   \n",
      "4019     -118.49     34.18                31.0       3073.0           674.0   \n",
      "12107    -117.32     33.99                27.0       5464.0           850.0   \n",
      "2398     -118.91     36.79                19.0       1616.0           324.0   \n",
      "\n",
      "       population  households  median_income  median_house_value  \\\n",
      "5241       2184.0       960.0         8.2816            500001.0   \n",
      "10970      1669.0       651.0         4.6111            240300.0   \n",
      "20351      2110.0       876.0         3.0119            218200.0   \n",
      "6568       1018.0       322.0         4.1518            182100.0   \n",
      "13285      1067.0       302.0         4.0375            121300.0   \n",
      "...           ...         ...            ...                 ...   \n",
      "20519      2414.0      1075.0         1.9728             76400.0   \n",
      "17430      1402.0       471.0         1.9750            134000.0   \n",
      "4019       1486.0       684.0         4.8984            311700.0   \n",
      "12107      2400.0       836.0         4.7110            133500.0   \n",
      "2398        187.0        80.0         3.7857             78600.0   \n",
      "\n",
      "      ocean_proximity  \n",
      "5241        <1H OCEAN  \n",
      "10970       <1H OCEAN  \n",
      "20351       <1H OCEAN  \n",
      "6568           INLAND  \n",
      "13285          INLAND  \n",
      "...               ...  \n",
      "20519          INLAND  \n",
      "17430      NEAR OCEAN  \n",
      "4019        <1H OCEAN  \n",
      "12107          INLAND  \n",
      "2398           INLAND  \n",
      "\n",
      "[4128 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "# pandas 分层贴标签\n",
    "housing[\"income_cat\"]=pd.cut(housing[\"median_income\"],\n",
    "                             bins=[0,1.5,3.0,4.5,6.,np.inf],\n",
    "                            labels=[1,2,3,4,5])\n",
    "#print(housing[\"income_cat\"])\n",
    "#housing[\"income_cat\"].hist()\n",
    "# 根据标签分类进行抽取样本 StratifiedShuffleSplit(分层随机分割)\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split=StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n",
    "\"\"\"\n",
    "n_splits：是将训练数据分成train/test对的组数，可根据需要进行设置，默认为10\n",
    "test_size和train_size： 是用来设置train/test对中train和test所占的比例。例如：\n",
    "1.提供10个数据num进行训练和测试集划分\n",
    "2.设置train_size=0.8 test_size=0.2\n",
    "random_state：随机数种子，和random中的seed种子一样，保证每次抽样到的数据一样，便于调试\n",
    "\"\"\"\n",
    "z=split.split(housing, housing[\"income_cat\"])\n",
    "# 因为n_splits=1 则 z中只有 一对 (train_index_array,test_index_array)\n",
    "housing=load_housing_data(\"./csv/housing.csv\")\n",
    "for train_index,test_index in z:\n",
    "    print(housing.loc[train_index],housing.loc[test_index])\n",
    "    strat_train_set=housing.loc[train_index]\n",
    "    strat_test_set=housing.loc[test_index]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-police",
   "metadata": {},
   "source": [
    "## Scikit-Learn分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sacred-employment",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set=train_test_split(housing,test_size=0.2,random_state=42)\n",
    "train_set.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worthy-reporter",
   "metadata": {},
   "source": [
    "### `从数据探索和可视化中获得洞见`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cubic-divorce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing=strat_train_set.copy()#使用训练集\n",
    "#散点图\n",
    "housing.plot(kind=\"scatter\",x=\"longitude\",y=\"latitude\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ranking-yield",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将alpha选项设置为0.1,可以更清楚看出高密度数据点的位置\n",
    "housing.plot(kind=\"scatter\",x=\"longitude\",y=\"latitude\",alpha=0.1)# alpha点的透明度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "quantitative-milan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x243d0bf7100>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每个圆的半径代表每个区域的人口数量(s),颜色代表价格(c),我们使用名为jet的预定义颜色表\n",
    "#(cmap)来进行可视化，颜色范围从蓝色到红色\n",
    "housing.plot(kind=\"scatter\",x=\"longitude\",y=\"latitude\",alpha=0.4,\n",
    "            s=housing[\"population\"]/100,label=\"population\",figsize=(10,7),\n",
    "            c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serious-shadow",
   "metadata": {},
   "source": [
    "#### 寻找相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infrared-atlantic",
   "metadata": {},
   "source": [
    "由于数据集不大，可以使用corr()方法计算出每对属性之间的标准相关系数(皮尔逊r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tropical-portuguese",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix=housing.corr()\n",
    "#看看每个属性对房价中位数的相关性\n",
    "print(corr_matrix[\"median_house_value\"].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accredited-fisher",
   "metadata": {},
   "source": [
    "### `机器学习算法的数据准备`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "floral-extension",
   "metadata": {},
   "outputs": [],
   "source": [
    "#回到一个干净的训练集\n",
    "housing=strat_train_set.drop(\"median_house_value\",axis=1)\n",
    "housing_labels=strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-treasury",
   "metadata": {},
   "source": [
    "#### 数据清理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dental-butler",
   "metadata": {},
   "source": [
    "三种解决方案:\n",
    "* 放弃这些相应的区域\n",
    "* 放弃整个属性\n",
    "* 将缺失值设为某个值(0,平均数或者中位数等)\n",
    "通过 DataFrame的dropna() drop() fillna()方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spatial-entity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total_bedrooms中有缺失值\n",
    "housing.dropna(subset=[\"total_bedrooms\"])# 方案1\n",
    "housing.drop(\"total_bedrooms\",axis=1) #方案2\n",
    "median=housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median,inplace=True)\n",
    "housing.info()#可以缺失值已经处理了"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hidden-rehabilitation",
   "metadata": {},
   "source": [
    "#### Scikit-Learn中的缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "middle-output",
   "metadata": {},
   "outputs": [],
   "source": [
    "#需要创建一个SimpleImputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer=SimpleImputer(strategy=\"median\")#采用相应属性中位数补上\n",
    "#创建一个没有文本属性ocean_proximity的数据副本\n",
    "housing_num=housing.drop(\"ocean_proximity\",axis=1)#去掉ocean_proximity列然后返回副本\n",
    "#使用fit()方法将imputer实例适配到训练数据\n",
    "imputer.fit(housing_num)\n",
    "#imputer仅仅只是计算了每个属性的中位数值，并将结果存储在实例变量的statstics_\n",
    "print(imputer.statistics_)\n",
    "print(housing_num.median().values)\n",
    "print(housing_num.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "precious-inspector",
   "metadata": {},
   "outputs": [],
   "source": [
    "#现在我们可以使用这个训练有素的imputer将缺失值替换成中位数值\n",
    "X=imputer.transform(housing_num)\n",
    "print(X)#结果是一个包含转换后特征的NumPy数组\n",
    "#将它放回pandas DataFrame\n",
    "housing_tr=pd.DataFrame(X,columns=housing_num.columns,index=housing_num.index)\n",
    "print(housing_tr.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acting-bracket",
   "metadata": {},
   "source": [
    "#### 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "italian-flashing",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat=housing[[\"ocean_proximity\"]]#获得副本\n",
    "housing_cat.head(10)#查看表的前10个实例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "anticipated-allocation",
   "metadata": {},
   "source": [
    "   对于文本而言计算机更喜欢数字，我们可以使用Scikit-Learn的OrdinalEncoder类来为其值种类进行编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "roman-founder",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "#创建实例\n",
    "ordinal_encoder=OrdinalEncoder()\n",
    "housing_cat_encoded=ordinal_encoder.fit_transform(housing_cat)#获得编码后的\n",
    "print(housing_cat_encoded[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "english-display",
   "metadata": {},
   "source": [
    "可见这与上面的种类进行了编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "designing-background",
   "metadata": {},
   "outputs": [],
   "source": [
    "#可以使用categories_属性获得编码对应的种类\n",
    "class_array=ordinal_encoder.categories_\n",
    "print(class_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proved-montgomery",
   "metadata": {},
   "source": [
    "则为   '<1H OCEAN',  'INLAND',  'ISLAND',     'NEAR BAY',   'NEAR OCEAN'\n",
    "编码为 0            1        2            3         4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parliamentary-setup",
   "metadata": {},
   "source": [
    "##### 独热向量种类编码(Scikit-Learn OneHotEncoder编码器)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forward-essex",
   "metadata": {},
   "source": [
    "使用独热编码器解决的问题是，为了避免认为编码相差小的比相差大的更相似  \n",
    "编码为形如 001  010  100 的形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "buried-princess",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "#创建实例\n",
    "cat_encoder=OneHotEncoder()\n",
    "housing_cat_1hot=cat_encoder.fit_transform(housing_cat)\n",
    "#print(housing_cat_1hot)\n",
    "#这里输出的是一个SciPy稀疏矩阵，而不是NumPy数组\n",
    "#如果像将其转换为NumPy数组，只需要调用toarray()方法\n",
    "print(housing_cat_1hot.toarray())\n",
    "#可以使用编码器的categories_实例变量来得到类别列表\n",
    "print(cat_encoder.categories_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-command",
   "metadata": {},
   "source": [
    "### `自定义转换器`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "special-spread",
   "metadata": {},
   "source": [
    "略"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loaded-catalog",
   "metadata": {},
   "source": [
    "### `特征缩放`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baking-turtle",
   "metadata": {},
   "source": [
    "同比例缩放所有属性的两种常用方法是最小-最大缩放和标准化  \n",
    "* 最小-最大缩放(归一化)  \n",
    "Scikit-Learn提供了MinMaxScaler的转换器  \n",
    "* 标准化  \n",
    "Scikit-Learn提供了一个标准化的转换器StandadScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-accreditation",
   "metadata": {},
   "source": [
    "### `转换流水线`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outer-bahrain",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "num_pipeline=Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy=\"median\")),\n",
    "    ('std_scaler',StandardScaler()),\n",
    "])\n",
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs=list(housing_num)\n",
    "cat_attribs=[\"ocean_proximity\"]\n",
    "full_pipeline=ColumnTransformer([\n",
    "    (\"num\",num_pipeline,num_attribs),\n",
    "    (\"cat\",OneHotEncoder(),cat_attribs)\n",
    "])\n",
    "housing_prepared=full_pipeline.fit_transform(housing)\n",
    "print(housing_prepared)"
   ]
  }
 ],
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